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A Panel Data Toolbox for MATLAB

Author

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  • Álvarez, Inmaculada C.
  • Barbero, Javier
  • Zofío, José L.

Abstract

Panel Data Toolbox is a new package for MATLAB that includes functions to estimate the main econometric methods of balanced and unbalanced panel data analysis. The package includes code for the standard fixed, between and random effects estimation methods, as well as for the existing instrumental panels and a wide array of spatial panels. A full set of relevant tests is also included. This paper describes the methodology and implementation of the functions and illustrates their use with well-known examples. We perform numerical checks against other popular commercial and free software to show the validity of the results.

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  • Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2017. "A Panel Data Toolbox for MATLAB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i06).
    • Handle: RePEc:jss:jstsof:v:076:i06
    DOI: http://hdl.handle.net/10.18637/jss.v076.i06
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    References listed on IDEAS

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    1. Badi H. Baltagi, 2021. "Simultaneous Equations with Error Components," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 157-186, Springer.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    3. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2013. "A Generalized Spatial Panel Data Model with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 650-685, August.
    4. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    5. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    6. Baltagi, Badi H. & Liu, Long, 2011. "Instrumental variable estimation of a spatial autoregressive panel model with random effects," Economics Letters, Elsevier, vol. 111(2), pages 135-137, May.
    7. Badi H. Baltagi, 2021. "Simultaneous Equations with Error Components," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 157-186, Springer.
    8. Jan Mutl & Michael Pfaffermayr, 2011. "The Hausman test in a Cliff and Ord panel model," Econometrics Journal, Royal Economic Society, vol. 14, pages 48-76, February.
    9. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    10. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    11. Baltagi, Badi H. & Li, Qi, 1992. "A Note on the Estimation of Simultaneous Equations with Error Components," Econometric Theory, Cambridge University Press, vol. 8(1), pages 113-119, March.
    12. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    13. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, February.
    14. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    15. Wallace, T D & Hussain, Ashiq, 1969. "The Use of Error Components Models in Combining Cross Section with Time Series Data," Econometrica, Econometric Society, vol. 37(1), pages 55-72, January.
    16. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    17. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
    18. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    19. repec:dgr:rugsom:03c27 is not listed on IDEAS
    20. Baltagi, Badi H. & Liu, Long, 2009. "A note on the application of EC2SLS and EC3SLS estimators in panel data models," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2189-2192, October.
    21. Elhorst, J. Paul, 2003. "Unconditional maximum likelihood estimation of dynamic models for spatial panels," Research Report 03C27, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    22. Piras, Gianfranco, 2010. "sphet: Spatial Models with Heteroskedastic Innovations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i01).
    23. Gianfranco Piras, 2013. "Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 370-388, September.
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    Cited by:

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    2. Ding Luo & Oded Cats & Hans Lint, 2020. "Can passenger flow distribution be estimated solely based on network properties in public transport systems?," Transportation, Springer, vol. 47(6), pages 2757-2776, December.
    3. Peter Nannestad, 2019. "Strategic Tax-Setting in Danish Municipalities? A First Look at the Evidence 2007 - 2017," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2019-10-ccr, Condorcet Center for political Economy.
    4. Gizem Umut Doğan & Gizem Umut Doğan, Aslıhan Kabadayı, 2015. "Determinants of Internal Migration in Turkey: A Panel Data Analysis Approach," Border Crossing, Transnational Press London, UK, vol. 2015(1502), pages 16-24, May.
    5. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2016. "A spatial autoregressive panel model to analyze road network spillovers on production," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 83-92.
    6. Vlad-Cosmin Bulai & Alexandra Horobeț, 2019. "Is The Oil Price a Determinant of Employment in Oil Intensive Romanian Communities?," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 12(3), pages 7-13, December.
    7. Marcos Herrera & Jesus Mur & Manuel Ruiz-Marin, 2017. "A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix," Working Papers 18, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    8. Arash Nayebyazdi, 2017. "The Relationship Between Democracy And Economic Growth In Muslim Mena Countries (Spatial Econometric Approach)," Journal of Smart Economic Growth, , vol. 2(3), pages 123-155, December.
    9. Gizem Umut Doğan & Gizem Umut Doğan, Aslıhan Kabadayı, 2015. "Determinants of Internal Migration in Turkey: A Panel Data Analysis Approach," Border Crossing, Transnational Press London, UK, vol. 5(1-2), pages 16-24, January-J.
    10. Raphael Asada & Tamás Krisztin & Fulvio di Fulvio & Florian Kraxner & Tobias Stern, 2020. "Bioeconomic transition?: Projecting consumption‐based biomass and fossil material flows to 2050," Journal of Industrial Ecology, Yale University, vol. 24(5), pages 1059-1073, October.
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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